45 results about "Random search algorithm" patented technology

According to some embodiments, a thermostat obtains real-time energy prices from a electricity grid. It may also obtain additional data from external data sources, such as predicted energy prices or weather predictions. The thermostat attempts to find a control strategy for when to switch available aggregates that may include furnaces and air conditioners on and off. In order to solve this integer programming problem, the thermostat uses a random search algorithm. According to some embodiments, various data sources, such as day-ahead prices and real-time prices, are combined into forecasts of electricity prices for the present and future time periods. In some embodiments, a thermostat selects predictively between heating or cooling by letting outside air in or by using heating and cooling aggregates. Additional embodiments are discussed and shown.

The invention discloses an optimization algorithm of a low-voltage transformer area line loss neural network, and the algorithm comprises the steps: 1) carrying out the preprocessing of the original distribution transformer side data and user side data of a low-voltage transformer area, and obtaining the line loss characteristic indexes of the low-voltage transformer area; 2) performing clustering analysis on the low-voltage transformer area data, dividing the data into four classification samples, and performing modeling on the whole sample and the four classification samples by adopting linear regression, r-tree and K-nearest neighbor algorithms; 3) screening out four characteristic parameters related to the grid structure and the load of the transformer area through a main factor analysis method; and 4) establishing a BP neural network model, setting four input ends and one output end which are respectively corresponding to the four characteristic parameters and the line loss rates, and optimizing input variables of the neural network by applying a genetic algorithm and / or a particle swarm algorithm until data convergence. According to the characteristics of low-voltage transformer area line loss calculation, two random search algorithms, namely the genetic algorithm and the particle swarm algorithm, are applied to optimization of the initial threshold value and the weight value of the BP neural network, and the precision and the speed of network training are improved.

The invention provides an elastic vector wave field numerical simulation method and system based on a random search algorithm, computer equipment and a computer readable storage medium, and relates tothe technical field of seismic exploration. The method comprises the following steps: constructing an improved particle swarm algorithm; According to the improved particle swarm optimization algorithm, carrying out optimization solution on an objective function containing a finite difference coefficient to obtain an optimized finite difference coefficient; And carrying out numerical simulation onthe elastic vector wave field according to a finite difference operator formed by the optimized finite difference coefficient. The invention designs a random search algorithm. the algorithm is an improved particle swarm algorithm; According to the elastic vector wave field numerical simulation method, the objective function containing the finite difference coefficient is optimized and solved through the improved particle swarm algorithm, the optimized finite difference coefficient is obtained, then the finite difference operator composed of the finite difference coefficient is used for conducting elastic vector wave field numerical simulation, and the elastic vector wave field numerical simulation precision and efficiency are improved.

According to some embodiments, a thermostat obtains real-time energy prices from a electricity grid. It may also obtain additional data from external data sources, such as predicted energy prices or weather predictions. The thermostat attempts to find a control strategy for when to switch available aggregates that may include furnaces and air conditioners on and off. In order to solve this integer programming problem, the thermostat uses a random search algorithm. According to some embodiments, various data sources, such as day-ahead prices and real-time prices, are combined into forecasts of electricity prices for the present and future time periods. In some embodiments, a thermostat selects predictively between heating or cooling by letting outside air in or by using heating and cooling aggregates. Additional embodiments are discussed and shown.

The invention relates to a method for solving the selected harmonic eliminated switching angle of a ladder wave multilevel converter. Power reduction is performed on an equation set by use of an elementary symmetric polynomial, and the equation set is triangularized by use of a Groebner basis algorithm; the solution of a multivariate higher-order polynomial equation set is converted into the solution of two univariate higher-order polynomial equations and a set of single-variable linear equations; all solutions of a harmonic elimination equation set can be obtained based on constraint conditions; and the optimal switching angle can be worked out by evaluating the total harmonic of each set of solutions. Compared with the current commonly-used numerical algorithm and random search algorithm, no initial value needs to be given, and all solutions of the harmonic elimination equation set can be obtained. Compared with other existing algebraic algorithms, the amount of calculation is greatly reduced, more switching angles can be processed, and the method has higher practical values.

Described here are systems and methods for knowledge-based brachytherapy planning. These systems and methods are capable of automatically generating treatment plans for prostate brachytherapy in clinically relevant times. Primitive features computed from anatomical contours of the target organ are used to retrieve a template plan from a database. The template plan is then adjusted in a stochastic search algorithm.

The invention relates to a query optimization method based on a simulated annealing algorithm. The method comprises the steps that a data query optimization process is divided into a model building part, a strategy space resolving part and an optimization part, then the simulated annealing algorithm is led in, all strategy space subsets are searched in a parallel mode, a final solution is obtained from each subset, and the optimal solution is obtained after the final solutions are compared. Compared with other intelligent optimization algorithms, the simulated annealing algorithm can effectively avoid a local extremum and shorten the optimization time. In addition, due to the utilization of parallel searching, the searching range of the simulated annealing algorithm is enlarged, and the influence on searching precision by local search characteristics of the simulated annealing algorithm can be reduced. Compared with the probability for searching an optimal strategy by a traditional local random searching algorithm, the probability for obtaining the optimal strategy by the query optimization method based on the simulated annealing algorithm is improved obviously. The query speed of a database is improved, the relative time of query optimization is shortened, and the probability for obtaining the optimal strategy is improved.

The invention provides an oil deposit well pattern and injection-production scheme optimum design method based on a balanced water drive idea. The method comprises the steps of 1, collecting and arranging block geological and development related data; 2, setting optimizing related parameters, and completing the well pattern and injection-production optimization preparation work; 3, adopting an oildeposit engineering method, predicting the displacement condition on each direction under current well location and injection-production parameters and performing quantitative evaluation; 4, adoptinga global random search algorithm, optimizing and generating novel well location / injection-production parameters, and predicting and evaluating the displacement condition in each injection-productiondirection under novel well location injection-production parameters; 5, arranging the optimization result, forming a well pattern injection-production design scheme, and performing on-site implementation. According to the method, the water drive development index calculation method built on the basis of the oil deposit engineering idea is combined with the optimization theory, the global random search algorithm is adopted for automatic solving, the calculation efficiency is ensured while the well pattern and injection-production scheme matched with the actual oil deposit is obtained, and the oil deposit recovery efficiency is improved.

The invention provides a public transportation multi-source data fusion based IC card swiping station matching method. The method is characterized in that data resources of a card swiping system and a GPS vehicle movement recording system are fused; and the optimal time lapse amount is obtained based on an adaptive random search algorithm to eliminate the time difference between the card swiping system and the GPS vehicle movement recording system so as to realize IC card swiping station matching. According to the IC card swiping station matching method, equipment regulation and changing is not required, while equipment adding is not required as well; therefore, low computation burden and high computation speed are realized; and the mutual matching between the IC card numbers and card swiping stations can be effectively realized, so that guest flow statistics and data mining can be facilitated.

The invention discloses a coal-fired boiler exhaust gas temperature prediction method and system based on a LightGBM and a random search method, and solves the problems that an existing neural network model is liable to fall into a local minimum value and is liable to over-fit, and a support vector machine model is not suitable for large sample learning. The method comprises the steps of collecting historical operation data, performing data cleaning and normalization, performing feature selection according to mutual information entropy, constructing a model by adopting a LightGBM algorithm, optimizing hyper-parameters by adopting a random search algorithm, and obtaining an optimal model for verification application. According to the method, the LightGBM and the random search algorithm are adopted to establish and optimize the prediction model, the overfitting phenomenon is effectively prevented, the model generalization ability is excellent, a large sample learning strategy is supported, training is more efficient, the calculation speed is higher, lower model deviation can be achieved, meanwhile, the random search algorithm is combined, an optimal hyper-parameter combination is found, and the prediction accuracy is improved. The precision of the model is further improved, and a high-performance coal-fired boiler exhaust gas temperature prediction model is obtained.

The invention discloses a method and system for optimizing the arrangement of large-scale offshore wind power clusters. The method includes: S1: dividing a large-scale wind power cluster area into a plurality of identical small wind farm areas; S2: determining from the large-scale wind power cluster The medium-sized wind farm of the wind power cluster is used as the optimization object, and the shape of the optimization object is the same as that of the large-scale wind power cluster; S3: Use the random search algorithm to determine the wind turbine arrangement in the small wind farm area with the smallest wake loss from the candidate points of the wind turbine arrangement cloth; S4: Calculate the wake loss in the large-scale wind power cluster area. When the wake loss in the large-scale wind power cluster area does not meet expectations, expand the scale of the optimization object, and skip to step S3 until the wake loss in the large-scale wind power cluster area meets the expectations. expected. By replacing the entire wind power cluster with a smaller-scale wind farm as the optimization object, the invention can greatly shorten the optimization time and cost, and better balance the calculation speed and the wake loss.

Described here are systems and methods for knowledge-based brachytherapy planning. These systems and methods are capable of automatically generating treatment plans for prostate brachytherapy in clinically relevant times. Primitive features computed from anatomical contours of the target organ are used to retrieve a template plan from a database. The template plan is then adjusted in a stochastic search algorithm.

The invention discloses a WSAN actuator task distribution method based on BA-BPNN data fusion, and the method employs a BA optimization BP neural network to build a data fusion model. The method specifically comprises the steps: employing a bat algorithm to optimize the weight value and threshold value of the BP neural network, building a data fusion model, carrying out the data fusion of the sensor node information, and obtaining the task distribution information of an actuator node. The bat algorithm is a meta heuristic type group intelligent optimization algorithm, employs an echo positioning method of a miniature bat under the condition of different transmitting speeds and responses, can achieve a precise capturing and obstacle avoidance random search algorithm. The BP neural network is a multilayer feedforward neural network which can search a global optimal value in a training process, and can increase the convergence rate of the network. The method searches the optimal parameter of the BP neural network through the positioning updating of bats, is more precise in data fusion, and is more reasonable in task distribution of an actuator.